AI-driven Mobile Apps: an Explorative Study

1 code implementation • 3 Dec 2022 • Yinghua Li, Xueqi Dang, Haoye Tian, Tiezhu Sun, Zhijie Wang, Lei Ma, Jacques Klein, Tegawende F. Bissyande

In this paper, we conduct the most extensive empirical study on 56, 682 published AI apps from three perspectives: dataset characteristics, development issues, and user feedback and privacy.

Predicting Patch Correctness Based on the Similarity of Failing Test Cases

1 code implementation • 28 Jul 2021 • Haoye Tian, Yinghua Li, Weiguo Pian, Abdoul Kader Kaboré, Kui Liu, Andrew Habib, Jacques Klein, Tegawendé F. Bissyande

Then, after collecting a large dataset of 1278 plausible patches (written by developers or generated by some 32 APR tools), we use BATS to predict correctness: BATS achieves an AUC between 0. 557 to 0. 718 and a recall between 0. 562 and 0. 854 in identifying correct patches.

Representation Learning

Super-Resolution of Brain MRI Images using Overcomplete Dictionaries and Nonlocal Similarity

no code implementations • 13 Feb 2019 • Yinghua Li, Bin Song, Jie Guo, Xiaojiang Du, Mohsen Guizani

The sparsity and self-similarity of the image blocks are taken as the constraints.

Compressive Sensing Image Super-Resolution